Optimization of Ads Using Reinforcement Learning and Comparison of Algorithms

Abstract

Ads optimization is the process of maximizing the effectiveness and profitability of advertising campaigns by improving targeting, messaging, and delivery strategies. This involves using data-driven techniques to analyze user behavior, identify key performance metrics, and optimize ad campaigns to achieve specific business goals, such as increasing conversions or reducing acquisition costs. Ads optimization can be applied to various types of advertising, including search engine marketing, social media advertising, display ads, and video ads. Common techniques used in ads optimization include A/B testing, machine learning algorithms, and predictive modeling. Ads optimization has become an essential component of modern digital marketing, as it allows advertisers to achieve higher ROI and better engage with their target audience. Key words: Upper Confidence Bound; Ads Optimization; Thompson Sampling; Reinforcement Learning